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14th International Conference on High Performance Computing & Simulation (HPCS)

作者： Caymes-Scutari, Paola Bianchini, German Sikora, Anna Margalef, Tomas UTN FRM Lab Invest Comp Paralelo Distribuido LICPaD Mendoza Argentina Consejo Nacl Invest Cient & Tecn Mendoza Argentina Univ Autonoma Barcelona High Performance Comp Applicat Sci & Engn Res Grp Barcelona Spain

ISBN: (纸本)9781509020881

The use of high performance computing has been gaining more and more followers in the different branches of Science and Engineering given the potential offered to cleat with complex and big problems. However, the parallel programming paradigm involves additional aspects to the merely functional, which could provoke different kinds of bottlenecks in the performance of the applications. Such diff culties may represent critical obstacles, especially for the non-expert users. In this work, we consider the limitations of the automatic development of parallel applications, and the obstacles in the performance tuning process, and we propose a built-in environment that integrates these both processes in a transparent manner. We call our proposal as Environment for automatic development and tuning of parallel Applications (EPA). EPA follows a cooperative approach: (i) It provides an interface to guide the user in the depiction of the pair problem-solution by completing a form;(ii) such information is automatically formalized in a specif cation;(iii) the specif ed information is automatically used by EPA to instantiate an instrumented skeleton;(iv) the skeleton of the parallel application is a priori enriched in EPA with tuning instrumentation, for monitoring, analysis and tuning actions;(v) the tunable parallel application is automatically generated by EPA;(vi) the user is able to execute the application with no more effort than having completed a form. Clearly, EPA makes transparent the process of code generation and instrumentation, which represents an important advantage especially for non-expert users. Given that the parallelization of any pair problem-solution is hard to generalize, the environment proposes to tackle different parallel problem solvers. The tuning knowledge injected in the generated applications is based on performance models, which makes the decision-making process concise. The Environment could include as problem solvers as needed. In this article, we

关键词： parallel paradigm automatic development automatic performance tuning dynamic tuning genetic algorithms parallel genetic algorithm

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OPF with Envirenmental Constraints with SVC Controller using Decomposed parallel GA: Application to the Algerian Network

OPF with Envirenmental Constraints with SVC Controller using...

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General Meeting of the IEEE-Power-and-Energy-Society

作者： Mahdad, B. Bouktir, T. Srairi, K. Univ Biskra Algeria Dept Elect Engn Biskra Algeria Oum Elbouaghi Univ Dept Elect Engn Oum El Bouaghi Algeria

ISBN: (纸本)9781424442409

Due to the rapid increase of electricity demand, consideration of environmental constraints in optimal power flow (OPF) problems is increasingly important. In Algeria up to 90% of the electricity demand produced by thermal generators (vapor, gas), in order to keep the emission of gaseous pollutants like sulfur dioxide (SO2) and Nitrogen (NO2) under the admissible ecological limits, many conventional and global optimization methods proposed to study the trade-off relation between fuel cost and emissions. This paper presents an efficient decomposed parallel GA to solve the multi objective environmental/economic dispatch problem. At the decomposed stage the length of the original chromosome is reduced successively and adapted to the topology of the new partition. Two sub problems are proposed: the first sub problem related to the active power planning to minimize the total fuel cost, and the second sub problem is a reactive power planning to make fine corrections to the voltage deviation and reactive power violation using a specified number of shunt dynamic compensators named Static Var Compensators (SVC). To validate the robustness of the proposed approach, the algorithm proposed tested on the Algerian 59-bus network test and compared with conventional method and with global optimization methods (GA, FGA, and ACO). The results show that the approach proposed can converge to the near solution and obtain a competitive solution at critical situation and with a reasonable time.

关键词： Environmental economic dispatch Dynamic control parallel genetic algorithm multi-objective System loadability FACTS SVC Optimal power flow System security Planning and control

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A Novel Bagging Ensemble Approach for Variable Ranking and Selection for Linear Regression Models 12

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12th IAPR Workshop on Multiple Classifier Systems (MCS)

作者： Zhang, Chun-Xia Zhang, Jiang-She Wang, Guan-Wei Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Shaanxi Peoples R China Xian Technol Univ Sch Mechatron Engn Xian 710021 Shaanxi Peoples R China

ISBN: (纸本)9783319202488;9783319202471

With respect to variable selection for linear regression models, a novel bagging ensemble method is developed in this paper based on a ranked list of variables. Specifically, a mixed importance measure is assigned to each variable according to the order that it is selected by stepwise search algorithm into the final model as well as the improvement resulted from its inclusion. Considering that small permutations in training data may lead to some changes in the order that the variables enter the final model, the above process is repeated for multiple times with each executed on a bootstrap sample. Finally, the importance measure of each variable is averaged across the bootstrapping trials. The experiments conducted with some simulated data demonstrate that the novel method compares favorably with some other variable selection techniques.

关键词： Variable selection Variable ranking Bagging Stepwise search algorithm parallel genetic algorithm Ensemble learning

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Automatic Arabic Text Summarization for Large Scale Multiple Documents Using genetic algorithm and MapReduce 2

Automatic Arabic Text Summarization for Large Scale Multiple...

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2nd Palestinian International Conference on Information and Communication Technology (PICICT)

作者： Al Breem, Sulaiman N. Baraka, Rebhi S. Islamic Univ Gaza Informat Technol Dept Gaza Palestine Islamic Univ Gaza Fac Informat Technol Gaza Palestine

ISBN: (纸本)9781509065387

Multi document summarization focuses on extracting the most significant information from a collection of textual documents. Most summarization techniques require the data to be centralized, which may not be feasible in many cases due to computational and storage limitations. The huge increase of data emerging by the progress of technology and the various sources makes automatic text summarization of large scale of data a challenging task. We propose an approach for automatic text summarization of large scale Arabic multiple documents using genetic algorithm and MapReduce parallel programming model. The approach insures scalability, speed and accuracy in summary generation. It eliminates sentence redundancy and increases readability and cohesion factors between the sentences of summaries. The experiments resulted in acceptable precision and recall scores. This indicates that the system successfully identifies the most important sentences. In Addition to all to that, the approach provided up to 10 x speedup score, which is faster than on a single machine. Therefore, it can deal with large-scale datasets successfully. Finally, the efficiency score of the proposed approach indicates that the large data set utilizes the available resources up to 62%.

关键词： Text Summarization parallel genetic algorithm MapReduce Hadoop Text Mining

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Neural Network with Migration parallel GA for Adaptive Control of Integrated DE-PSO Parameters 8

Neural Network with Migration Parallel GA for Adaptive Contr...

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8th EUROSIM Congress on Modelling and Simulation (EUROSIM)

作者： Pham, Hieu Tooyama, Sousuke Hasegawa, Hiroshi Shibaura Inst Technol Funct Control Syst Tokyo Japan Shibaura Inst Technol Coll Syst Engn & Sci Tokyo Japan

ISBN: (纸本)9780769550732

This study develops an evolutionary strategy called DEPSO-GANN, which uses an artificial neural network (ANN) based on a parallel genetic algorithm (PGA) with migration for the adaptive control of integrated differential evolution (DE) and particle swarm optimization (PSO) to solve large-scale optimization problems, reduce calculation costs, and improve the stability of convergence towards the optimal solution. This approach combines the global search ability of DE and the local search ability of adaptive system with migration parallel GA. The proposed algorithm incorporates concepts from DE, PSO, PGA and neural networks (NN) to facilitate the adaptive control of parameters. DEPSO-GANN is applied to several numerical benchmark tests with multiple dimensions to evaluate its performance;it is also compared with other evolutionary algorithms (EAs) and memetic algorithms (MAs), which is shown to be statistically significantly superior to other EAs and MAs. We confirm satisfactory performance through various benchmark tests.

关键词： Adaptive Plan Differential Evolution Neural Network parallel genetic algorithm Particle Swarm Optimization

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A Globally DiversifiedIsland ModelPGA for Multimodal Optimization

A Globally DiversifiedIsland ModelPGA for Multimodal Optimiz...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Zhang, Li Feng He, Rong Renmin Univ China Sch Informat Beijing 100872 Peoples R China

ISBN: (纸本)9781479914883

Multimodal optimization aims to find multipleglobal and local optima as opposed to only the best optimum. parallel genetic algorithms (PGAs) provide a natural advantage for dealing with this issue, since they are multi-population based searching methodologies. For single population based evolutionary algorithms, a number of niching and multimodal optimization techniques have been proposed and successfully applied to cope with this problem. However, these approaches are definitely not applicable for PGAs, since due to communicational and computational costs it is very always impossible to obtain and compute global information of all the sub-populations during massive parallel evolution procedure. In this study, a new island model PGA, called local competitionmodel (LCM), is developed to cope with this issue. The new method only uses local information received from a few neighbouring subpopulations to reach a global diversification in which all the subpopulations are automatically allocated to differentareas of searching space so that they canconverge to multiple optima including both global optima and local optima. Finally, experimental studies on both real number optimization and combinatorial optimization are implemented to illustrate the performance of the new PGA model.

关键词： parallel genetic algorithm Island model Niching Multimodal optimization

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Optimization of RIS Placement for Satellite-to-Ground Coverage Enhancement

Optimization of RIS Placement for Satellite-to-Ground Covera...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Liu, Xingchen Xue, Liuxun Sun, Shu Tao, Meixia Shanghai China

ISBN: (纸本)9798350370218

In satellite-to-ground communication, ensuring re-liable and efficient connectivity poses significant challenges. The reconfigurable intelligent surface (RIS) offers a promising solution due to its ability to manipulate wireless propagation environments and thus enhance communication performance. In this paper, we propose a method for optimizing the placement of RISs on building facets to improve satellite-to-ground commu-nication coverage. We model satellite-to-ground communication with RIS assistance, considering the actual positions of buildings and ground users. The theoretical lower bound on the cover-age enhancement in satellite-to-ground communication through large-scale RIS deployment is derived. Then a novel optimization framework for RIS placement is formulated, and a parallel genetic algorithm is employed to solve the problem. Simulation results demonstrate the superior performance of the proposed RIS deployment strategy in enhancing satellite communication coverage probability for nonline-of-sight users. The proposed framework can be applied to various architectural distributions, such as rural areas, towns, and cities, by adjusting parameter settings. © 2023 IEEE.

关键词： parallel genetic algorithm Reconfigurable intelligent surfaces satellite-to-ground communication

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Improved parallel PSO Solution to Economic Dispatch with Practical Generator Constraints

Improved Parallel PSO Solution to Economic Dispatch with Pra...

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15th IEEE Mediterranean Electrotechnical Conference (MELECON 2010)

作者： Mahdad, Belkacem Srairi, K. Bouktir, T. Department of Electrical Engineering University of Biskra Algeria

ISBN: (纸本)9781424457953

This paper presents an improved parallel particle swarm optimization approach (IPPSO) based decomposed network for economic power dispatch with consideration of practical generator constraints. The range of partial power demand corresponding to the partial output powers near the global optimal solution is determined by a flexible decomposed network strategy and then the final optimal solution is obtained by parallel particle swarm optimization. To validate the robustness of the proposed IPPSO approach, it is tested to two test systems having nonconvex solution spaces, 26-bus (6 generating units) with consideration of valve point effects, network and with 15 generating units. The simulation results compared with recent global optimization methods (GA, MTS, SA, PSO and ICA-PSO). The outcome of the comparisons shows the effectiveness of the proposed IPPSO approach in terms of solution quality, consistency and reduced computational time compared to various methods available in the literature survey.

关键词： parallel genetic algorithm Decomposed Network PSO economic dispatch Optimal power flow System security Planning and control

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Multistrategy learning using genetic algorithms and neural networks for pattern classification

Multistrategy learning using genetic algorithms and neural n...

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1996 IEEE International Conference on Systems, Man and Cybernetics - Information, Intelligence and Systems

作者： Zhou, YH Zhang, ZH Lu, YC Shi, CY Laboratory of Intelligent Technology& Systems Department of Computer Science Tsinghua University Beijing China

ISBN: (纸本)7800033813

This paper introduces a two-level learning algorithm which combines parallel genetic algorithm (PGA) and backpropagation algorithm (BP) in order to evolve optimal subsets of discriminatory features for robust pattern classification. In this approach, PGA is used to explore the space of all possible subsets of a large set of candidate discriminatory features. For a given subset, BP is invoked to be trained according to related training data. The individuals of population are evaluated by the classification performance of the trained BP according to the testing data. This process iterates until a satisfactory subset is attained. We use the classification of handwritten numeral and structure of ionosphere for experiment. The results show that this multistrategy methodology improves the classification accuracy rate and the speed of training.

关键词： neural network parallel genetic algorithm multistrategy learning pattern classification

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Distributed AquaCrop simulation-nonlinear multi-objective dependent-chance programming for irrigation water resources management under uncertainty

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AGRICULTURAL WATER MANAGEMENT 2021年 247卷 106752-106752页

作者： Wang, Youzhi Guo, Shanshan Yue, Qing Mao, Xiaomin Guo, Ping China Agr Univ Ctr Agr Water Res China Beijing 100083 Peoples R China Minist Agr & Rural Affairs Wuwei Expt Stn Efficient Water Use Agr Wuwei 733000 Peoples R China

In the agricultural water resources system, the regional yield is hard to be simulated accurately under the impacts of spatial heterogeneities of soil, weather, and crop types. The optimal water allocation schemes may be not in accordance with actual conditions due to neglecting the actual crop growth process. Meanwhile, the uncertainties in the simulation-optimization models are not easy to be addressed. To deal with the above problems, this paper develops a framework of the distributed AquaCrop simulation nonlinear multi-objective dependent chance programming (distributed AquaCrop NMOFDCP) for irrigation water resources management under uncertainties. This developed model is applied to a case study of irrigation water resources management in the middle reaches of the Heihe River Basin in China. The 134 homogeneous decision-making units (DMUs) are divided to depict the spatial heterogeneities of the study area, and 472 decision variables (irrigation amount) for 134 DMUs are optimized. Moreover, the model deals with uncertainties expressed as fuzzy goals and tradeoffs relationships between objectives of the yield and water productivity, and measures the satisfactory degrees between objectives and their fuzzy goals. Two groups of Pareto solutions corresponding to the maximum satisfactory degree of the yield, and the maximum satisfactory degree of the water productivity are obtained by the parallel genetic algorithm (PGA) method respectively. In addition, water allocation, satisfactory degree of the yield, and satisfactory degree of water productivity are analyzed at the decision-making unit scale, crop scale, and irrigation-district scale separately. Besides the effects of three weather conditions and four soil types on the system's outputs are conducted. The results show that weather conditions and soil types have obvious effects on the system's outputs at three analysis scales, and different water allocation patterns at the growth period affect the yield and water

关键词： Distributed simulation-optimization model Distributed AquaCrop modeling Nonlinear multi-objective dependent-chance programming parallel genetic algorithm Uncertainty

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